Method for seismic facies interpretation using textural analysis and neural networks

ABSTRACT

Seismic facies are identified in a volume of seismic data, wherein, first, a plurality of initial textural attributes representative of the volume of seismic data are calculated. Next, a probabilistic neural network is constructed from the calculated initial textural attributes. Then, final textural attributes are calculated throughout the volume of seismic data. Finally, the calculated final textural attributes are classified using the constructed probabilistic neural network.

This application claims the benefit of U.S. Provisional Application No.60/236,577 filed Sept. 29, 2000.

FIELD OF THE INVENTION

This invention relates generally to the field of geophysicalprospecting. More particularly, the invention is a method ofcharacterizing and mapping seismic facies in seismic data.

BACKGROUND OF THE INVENTION

Seismic facies analysis is an important step in the interpretation ofseismic data for reservoir characterization. Seismic faciesinterpretations play a significant roll in initial basin exploration,prospect evaluation, reservoir characterization, and ultimately, fielddevelopment. A seismic facies is a stratigraphic unit or region that hasa characteristic reflection pattern distinguishable from those of otherareas. Regions of differing seismic facies are usually delineated usingdescriptive terms that reflect large-scale seismic patterns such asreflection amplitude, continuity, and internal configuration ofreflectors bounded by stratigraphic horizons.

The application and scale of seismic facies analysis variessignificantly, from basin wide applications to detailed reservoircharacterization. On a basin-wide scale, reconnaissance seismic faciesanalysis has been applied in the study of hydrocarbon systems to broadlyidentify regions of source, reservoir, and seal-prone regions. Theseregions are usually identified on the basis of their reflection geometryas well as amplitude strength and continuity. Regionally high-amplitude,semi-continuous reflectors are often used to identify potentialhydrocarbon-bearing reservoirs, such as deep-water channels, whilelow-amplitude continuous to semi-continuous regions can be used toidentify seal-prone units.

Seismic facies analysis can also be applied within a single reservoir tohelp constrain a detailed physical-property characterization. In theselocal-scale applications, definitions of continuity and amplitudegenerally do not have strict definitions, and are based on rock propertycalibration or environment of deposition interpretations. Assuming arelationship between seismic character and physical properties can bedemonstrated, seismic facies volumes can then be used to predict rockproperty distribution and condition geologic models.

The standard technology used for seismic facies analysis and mapping isa manual process where the seismic interpreter makes visual decisionsabout the character of the seismic reflection data within an interval ofinterest and plots these on a map. Seismic facies are then used for avariety of purposes, but primarily to interpret the distribution oflithofacies and rock properties. A skilled interpreter's perception,intuition, and experience contribute significantly to the success ofseismic facies studies. However, these same strengths can also causeseismic facies analysis to be a subjective, time consuming, and oftenlaborious task. Several related techniques have been used in the oilindustry to automate and enhance the interpretation of seismic faciesfrom seismic data.

R. J. Matlock and G. T. Asimakopoulos, “Can Seismic StratigraphyProblems be Solved Using Automated Pattern Analysis and Recognition?”,The Leading Edge, Geophys Explor, Vol. 5, no. 9, pp.51-55, 1986 lay outa conceptual framework for training of an algorithm, and thusautomation, of the seismic interpretation process. However, theseauthors do not demonstrate any working prototype or describe anyspecifics of the possible attributes or classification algorithms.

R. Vintner, K. Mosegaard, et al., “Seismic Texture Classification: AComputer-Aided Approach to Stratigraphic Analysis”, SEG InternationalExposition and 65th Annual Meeting, paper SL1.4, Oct. 8-13, 1995 and R.Vintner, K. Mosegaard, I. Abatzis, C. Anderson, V. O. Vejbaek, and P. H.

Nielson, “3D Seismic Texture Classification”, Society of PetroleumEngineers 35482, 1996, discuss textural analysis of seismic data as wellas classification of textural attributes using a version ofprincipal-component analysis and probability distributions. Thesepublications, while using textural analysis methods on seismic data, donot take advantage of probabilistic neural networks or the dynamic useof probability values to optimize the classification. These methods alsodo not utilize an interactive training scheme and the textural analysisis not dip-steered. The process of guiding a calculation by thestratigraphic layering defined by the dip of the seismic reflectors iscalled dip-steering.

D. Gao, “The First-Order and the Second-Order Seismic Textures:Implications for quantitative Seismic Interpretation and HydrocarbonExploration”, 1999, describes the use of standard textural analysis toproduce seismic textural attributes that quantify reflection strength,continuity, and geometry. This abstract does not, however, describemethods of classification of textural attributes. Specifically, Gao,1999, does not use a probabilistic neural network nor interactiveinterpreter training of the neural network. Additionally, the texturalanalysis is not dip-steered.

Turhan Taner, in combination with Rock Solid Images and the Consortiumfor Computation and Interpretive Use of Seismic Attributes, employs amethod in which various seismic attributes are used to interactivelytrain a neural network. However, textural attributes are not used andthe network employed is a fully-connected back-propagation neuralnetwork, rather than a probabilistic neural network.

P. Meldahl, R. Heggland, P. F. M. de Groot, and A. H. Brill, “TheChimney Cube, an Example of Semi-Automated Detection of Seismic Objectsby Directive Attributes and Neural Networks: Part I; Methodology”, “TheChimney Cube, an Example of Semi-Automated Detection of Seismic Objectsby Directive Attributes and Neural Networks: Part II; Interpretation”,and British Patent with International Publication No. WO 00/16125,“Method of Seismic Signal Processing” use seismic attributes tointeractively train a neural network and produce a facies volume.However, in the training and production of the chimney cube, only oneclass of item, instead of multiple classes, is focussed on andclassified at a time. Accordingly, only two final output nodes are usedin the neural network architecture. A probability cube is computed andthen, as a post-processing phase, on-off thresholds are drawn to decideif the object is of the class of interest or not. A complex Wigner-Radontransformation scheme is used for dip-steering the seismic attributes.The attributes are manually chosen for individual classes.

Elf Acquitaine, “Automatic Seismic Pattern Recognition”, FR 273892019970321 and EP 808467 19971126, describe a seismic trace-based methodfor seismic pattern recognition. Each seismic trace within auser-defined interval is decomposed into a user-defined number ofempirical-orthogonal functions. These derived functions are thenclassified using a neural network based classification algorithm, ratherthan interpreter-trained textural analysis.

Thus, there exists a need to generate, in a computationally efficientmanner, a process that enables the rapid, objective classification ofseismic data so that it can be exploited in the seismic facies mappingprocess. This process must also mimic the process employed by andresults obtained manually by the seismic interpreter.

SUMMARY OF THE INVENTION

The present invention is a method for identifying seismic facies in avolume of seismic data. First, a plurality of initial texturalattributes representative of the volume of seismic data are calculated.Next, a probabilistic neural network is constructed from the calculatedinitial textural attributes. Then, final textural attributes arecalculated throughout the volume of seismic data. Finally, thecalculated textural attributes are classified using the constructedprobabilistic neural network.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention and its advantages may be more easily understoodby reference to the following detailed description and the attacheddrawings in which:

FIG. 1 is a flow chart illustrating the method of an embodiment of thepresent invention;

FIG. 2 is an example seismic cross-section from a regional study;

FIG. 3A shows polygons selected for textural analysis of the exampleshown in FIG. 2;

FIGS. 3B-3G are facies classifications corresponding to the polygons inFIG. 3A;

FIG. 4 is a facies classification section for the example shown in FIG.2, as calculated by the method of the invention;

FIG. 5 is a dip section used in dip-steering the example shown in FIG.2;

FIG. 6 is a confidence section corresponding to the faciesclassification section shown in FIG. 4; and

FIG. 7 is a seismic facies volume calculated for the example shown inFIG. 2.

While the invention will be described in connection with its preferredembodiments, it will be understood that the invention is not limitedthereto. On the contrary, it is intended to cover all alternatives,modifications and equivalents that may be included within the spirit andscope of the invention, as defined by the appended claims.

DETAILED DESCRIPTION OF THE INVENTION.

The present invention is a method of recognizing and mapping seismicfacies in seismic data, particularly in seismic amplitude data, althoughthe method is applicable to other seismic attributes, as well. FIG. 1 isa flow chart illustrating the method of an embodiment of the presentinvention. First, in step 101, a 3D volume of seismic data is selected.Although 3D volumes of data are discussed, the method works equally wellfor 2D data sets. This seismic data volume will be used to calculate aseismic facies volume and corresponding confidence volumes. Preferably,the seismic data is seismic attribute or amplitude data, including, butnot limited to, near, far, and full-stack data.

Next, in step 102, at least one cross-section is selected from thevolume of seismic data from step 101. In step 103, a plurality ofpolygons are constructed on the selected cross-sections from step 102.The polygons need not be the same size, orientation, or from the samestratigraphic interval, but can be distributed throughout thecross-sections and the volume in any appropriate orientation orgeometry. Preferably, the polygons are constructed by digitizing them ona display of the selected cross-sections.

The polygons in step 103 are constructed to contain an imagerepresentative of a facies type from the volume of seismic data.Preferably, enough examples of each facies type of interest should beprovided to characterize the variation present in the input volume ofseismic data from step 101. The facies types will be represented byseismic texture. Seismic texture is a characteristic that quantifiesmany aspects of the standard seismic facies description performed by aseismic interpreter. Seismic texture is a quantitative measure of thereflection amplitude, continuity, and internal configuration ofreflectors. Seismic textures can be described as smooth or rough,small-scale or large-scale and are quantified through standardstatistical methods, described as textural attributes. Seismic textureis inherently a multi-trace seismic attribute, and thus is significantlydifferent from many traditionally calculated seismic attributes based onsingle traces. The analysis of seismic texture thus mimics thevisually-based analysis process of a seismic interpreter in a way thattraditional attribute analysis does not. An interpreter does not examineone or two traces at a time. Rather, the interpreter examines anensemble of traces as an image to render a classification. Thisdifferent analysis approach offers the potential to capture reflectiongeometry within an entire region of investigation

Textural analysis techniques describe the spatial organization of pixelvalues within a defined region, such as the above polygons on thecross-sections. In general, this region, the textural analysis window,is called a “texel”. One such technique used to quantify an image'stexture in a texel employs an image transformation that results inGray-Level Co-occurrence Matrices. Gray-Level Co-occurrence Matricesdescribe the spatial relationships between pixels of a small regionwithin the larger image, the texel. In practice, Gray-LevelCo-occurrence Matrices are computed in overlapping texels so that anytransition between textural classes within the entire image can be fullyobserved. The overlapping texels sweep across and down through the imageuntil the entire image is processed.

Gray-Level Co-occurrence Matrices are matrices with dimensions N×N, wereN is the number of gray levels used to quantify the image. For example,8 bit data has 28=256 gray levels, and a Gray-Level Co-occurrence Matrixconstructed from this image will be a matrix that has 256 rows and 256columns. Computation and analysis of Gray-Level Co-occurrence Matricesis an expensive computational task, with computational requirementsproportional to N2. Each element within the Gray-Level Co-occurrenceMatrix expresses the relative frequency of occurrence of two points,with respective pixel values i and j, at a distance D(d, θ) from oneanother within the texel. For example, if pixel A has value i and is adistance D from pixel B with value j, then the Gray-Level Co-occurrenceMatrix position i,j will be incremented by one. This process isperformed for each existing pixel set within the texel. In their mostgeneral application a Gray-Level Co-occurrence Matrix calculation canreflect both a transition in pixel values and a direction or “grain”within an image. Textural analysis via the construction of a Gray-LevelCo-occurrence Matrix from an image texel is effectively the two- (orthree-) dimensional extension of one-dimensional Markov Chain analysis.

The structure of seismically-derived Gray-Level Co-occurrence Matricescan be heuristically understood. In homogeneous regions, wherehomogeneity or continuity is defined in a given direction, differencesbetween pixel values will be low, and the elements close to the diagonalof the Gray-Level Co-occurrence Matrices will therefore have highervalues. Less homogeneous regions will yield higher differences betweenadjacent pixel values and resulting Gray-Level Co-occurrence Matriceswill therefore have higher values further away from the diagonal.Average pixel value also expresses itself in the Gray-LevelCo-occurrence Matrix. Regions of low amplitude have Gray-LevelCo-occurrence Matrices with values clustered near the center. Regionswith higher amplitude, on the other hand, have more distributedGray-Level Co-occurrence Matrix values, either along the diagonal forcontinuous textures or throughout the Gray-Level Co-occurrence Matrix inmore discontinuous textures.

In step 104, initial facies classifications are provided for thepolygons selected in step 103. Examples of facies classificationstypically used in the present invention include, but are not limited to,high amplitude continuous (HAC), high amplitude semi-continuous (HASC),moderate amplitude continuous (MAC), moderate amplitude semi-continuous(MASC), low amplitude continuous (LAC), low amplitude semi-continuous(LASC), chaotic, and transparent. Examples of the first sixclassifications are shown in FIGS. 3B, 3C, 3G, 3D, 3E, and 3F,respectively. In step 105, Gray-Level Co-occurrence Matrices are thusconstructed from the images in the constructed polygons from step 103for the facies from step 104. Statistical transformations of thesematrices then describe the spatial relationships between pixels of asmall region. In step 106, initial textural attributes are calculatedfrom the constructed Gray-Level Co-occurrence Matrices from step 104,using a user-defined moving window. This calculation can generally becalled the production of the seismic texture values in the seismic data.In order to closely mimic the process followed by a seismic interpreter,2D textural attributes are preferably calculated and then filtered intime slice to mimic a fully 3D operation. Alternatively, 3D texturalattributes can also be calculated and used to characterize the seismicfacies.

Gray-Level Co-occurrence Matrices are not efficiently interpreteddirectly, and are more effectively described by scalar statisticalmeasures, called textural attributes. Textural attributes can be dividedinto first- and second-order descriptors. First-order statisticsquantify the global distribution of pixel values within an image, andcan be calculated directly from a texel using standard statisticaltechniques even without an intermediate Gray-Level Co-occurrence Matrixtransformation. Average absolute amplitude and standard deviation ofamplitude values within a texel are examples of a first-order texturalattributes, and are useful in delineating amplitude anomalies andreflection strength. Derived attributes such as instantaneous amplitude,phase, and frequency can also be used to produce first-order statistics.

First-order statistics are a beginning approach toward a detailedtexture quantification, and although some geophysical regions can becoarsely defined from distinct intervals of pixel values, in general, anindividual texel cannot be adequately described on the basis of theirfirst-order statistics alone. For example, a high-amplitude chaoticregion of a seismic image cannot necessarily be separated from a high-or even moderate-amplitude continuous region using only averageamplitude values.

Second-order statistics of an image quantify the spatial relationshipsof pixels within the image, and are calculated via the intermediatetransform to the Gray-Level Co-occurrence Matrix. Second-orderstatistics, statistics of the Gray-Level Co-occurrence Matrix, capturetrace shape characteristics, reflection geometry, and reflectioncontinuity, in addition to amplitude strength. Second-order statisticsof a texel are a multi-trace, image attribute, which allows reflectiongeometry and continuity to be captured through analysis of thedip-steered Gray-Level Co-occurrence Matrix.

Textural attributes preferably used in the present invention include,but are not restricted to, textural homogeneity, inertia (also knows asthe element-difference moment or contrast), entropy, and energy (alsoknown as uniformity). The mathematical expressions of these texturalattributes are given as:

homogeneity=Σ_(i)Σ_(j)1/1+(i−j)² c _(ij),

inertia=1/(n−1)²Σ_(i)Σ_(j)(i−j)² c _(ij),

entropy=−1/2 log nΣ _(i)Σ_(j) c _(ij) log c _(ij),

energy=Σ_(i)Σ_(j) c _(ij) ²,

The first textural attribute, textural homogeneity, quantifies theamount of local similarities inside the texel. Because it is inverselyproportional to (i-j)2, local textural homogeneity will be larger forGray-Level Co-occurrence Matrices with elements concentrated near thediagonal. These Gray-Level Co-occurrence Matrices correspond to texturesof organized and poorly contrasted features with only a few gray levelsat the same distance and azimuth from one another. Lower values oftextural homogeneity will correspond to larger values of the Gray-LevelCo-occurrence Matrix further away from the diagonal of the matrix, thatis many differing gray levels that the same distance and azimuth. Thesecharacteristics make textural homogeneity particularly useful forquantifying continuity.

The second textural attribute, textural inertia, is indicative of thecontrast of the Gray-Level Co-occurrence Matrix, and is the oppositemeasure to textural homogeneity. Whereas textural homogeneity will below for a highly contrasted image, textural inertia will be high.

The third textural attribute, textural entropy, measures the lack ofspatial organization inside the computation window. Textural entropy ishigh when all elements of the Gray-Level Co-occurrence Matrix are equal,corresponding to a rough texture, and low then the texture is morehomogeneous or smoother.

The fourth textural attribute, textural energy, is also indicative ofthe spatial organization within the computational window. Texturalenergy is lowest when all elements of the Gray-Level Co-occurrenceMatrix are equal, the opposite of textural entropy. In this case, all ormost gray levels within the computational window are equally probable.This is characteristic of a rough texture. Conversely, the highestvalues of textural energy show the presence in the Gray-LevelCo-occurrence Matrix of high values. In this case, only a few graylevels are dominant. The region inside this computation window is morehomogeneous, or exhibits some regular character.

In step 107, a probabilistic neural network is constructed from theinitial textural attributes, along with their associated initial faciesclassifications, from steps 105 and 106, respectively. A neural networkis an interconnected assembly of simple processing elements. Theprocessing ability of the neural network is stored in the connectionstrengths, or weights, obtained by a process of adaptation to, orlearning from, a set of training patterns. One of the advantages ofneural networks is the ability to train or modify the connectionstrengths within the network to produce desired results. In aclassification application, a neural network can be thought of asspecial case of a supervised classification scheme in that the trainingof a neural network is a supervised exercise. Once sufficiently trainedon a number of calibration images, the neural network can then beapplied to the remaining images in a data volume.

Computationally, the connectivity of the nodes within a general neuralnetwork, the weights, modify an input vector of attributes and pass themodified values on to the next layer of the network. Through training,the weights of the network are modified such that on a specific set oftraining examples, modification of the input attribute vectors produce adesirable outcome. The training of a network and modification ofconnection weights results in the production of a decision surface forthe network. A decision surface is an n-dimensional surface that allowsthe network to separate the input training data into categories. One ofthe advantages of a neural network algorithm over more standardclassification schemes is the ability to produce non-linear boundaries.Typical classification or prediction problems commonly have only threelayers, an first, input layer; a second, “hidden” layer; and a third,output layer.

Probabilistic neural networks are parallel implementations of a standardBayesian classifier. A probabilistic neural network is a three-layernetwork that can efficiently perform pattern classification.Mathematically, these probabilistic neural networks are very analogousto kriging, where proximity to known points guide the classification andprediction of unknown points. In its standard form, the probabilisticneural network is not trained in the same way as the more-traditionalneural network described above. Rather, the training vectors simplybecome the weight vectors in the first layer of the network. Thissimpler approach gives probabilistic neural networks the advantage ofnot requiring extensive training. In seismic textural analysis, forexample, the textural attributes of the training images supply weightvectors in the first layer of the network. This results in a dramaticspeed advantage in the training phase over more traditional types ofneural network architectures, such as fully-connected back propagationarchitectures. Further, a probabilistic neural network tends togeneralize well, whereas more traditional networks, even with largeamounts of training data, are not guaranteed to converge and generalizeto data not used in the training phase.

When an input pattern is presented to a probabilistic neural network,the first, or input, layer computes distances from the input vector tothe training input vectors, and produces a vector whose elementsindicate how close the input is to a training input. The second layersums these contributions for each class of inputs to produce as its netoutput a vector of probabilities. This leads to another advantage ofusing probabilistic neural networks. This is the ability to extractclassification probabilities directly from the second, or hidden, layer,in addition to the classification of the maximum probability from thethird, or output, layer.

In the present invention, the input training points for theprobabilistic neural network constructed in step 107 are the initialtextural attributes from step 105 and the associated initial faciesclassifications from step 106. The output from the probabilistic neuralnetwork will be facies classifications (and a probability volume, to bediscussed below). The probabilistic neural network could then be used toclassify the entire volume of seismic data. However, at this point it ispreferred to make a quality control check and, if deemed necessary, tomodify or completely retrain the probabilistic neural network.

Thus, in step 108, the initial probabilistic neural network is used toclassify the facies in a portion of the volume of seismic data from step101. Preferably, this portion is one of the cross sections selected instep 102. In step 109, a determination is made whether the faciesclassification of the portion of the seismic data volume issatisfactory. If the determination is that the facies classification isnot satisfactory, then the process returns to step 103. The training setcan be modified either through deletion of existing polygons or additionof new polygons. The probabilistic neural network is then re-createdwith the modified training set, and again checked. This ability to trainand quality check the probabilistic neural network and theninteractively modify a pre-existing training set allows the presentinvention to reproduce a facies classification that an interpreter wouldhave produced manually. Only then will the process continue to classifythe entire seismic data volume. Thus, if the determination in step 109is that the partial facies classification is satisfactory, then theprocess continues to step 110.

In step 110, final facies classifications are calculated throughout thevolume of seismic data from step 101 using the probabilistic neuralnetwork constructed in step 107. This produces a seismic faciesclassification volume, based on the seismic texture attributes producedfrom the original, user-defined polygons.

The quality of the seismic facies volume is dependent upon the qualityof the input data. Decreasing quality of input data often occurs withincreasing depth in the subsurface. Using a single Gray-LevelCo-occurrence Matrix calculation window size for the entire volumecontributes to this negative effect. Results are improved by varying thewindow size throughout the volume. Preferably, the window size is madelarger as data frequency decreases with increasing depth. This modeworks in combination with the dynamically adjusted window size based ona user-defined confidence level. In a further alternative embodiment todeal with decreasing quality of seismic data, the data can be initiallyfiltered with a convolution or median filter to smooth the data prior toinput.

Finally, in step 111, a confidence volume is also created from theoutput of the probabilistic neural network. In an alternativeembodiment, the confidence volume can be used dynamically during thecalculation of the seismic facies classification volume in step 110. Ifa confidence falls below a user-defined level, the calculation windowsize can be automatically adjusted until the confidence level risesabove acceptable levels, and the facies is recalculated and reclassifiedaccordingly.

In a further alternative embodiment, the production of the Gray-LevelCo-occurrence Matrices in step 104 can be dip-steered. The stratigraphicframework of a particular geologic setting is an important aspect thatis always considered, albeit unconsciously, by the seismic interpreter.Seismic facies interpreters, for example, do not consider continuitysolely in the time- plane. Rather, they judge continuity following thestratigraphic layering defined by dip of seismic reflectors. Textureanalysis and construction of a Gray-Level Co-occurrence Matrix for atexel, as described above, is dependent on the look direction orazimuth, θ, in which the pixels within the texel are related. Texturalanalysis applied to seismic data is extremely sensitive to thestratigraphic framework of the texel, and must also follow thestratigraphic dip of the reflectors to properly mimic the processperformed by the human interpreter. Following the stratigraphic dip in aGray-Level Co-occurrence Matrix calculation maximizes the continuity ofthe image as expressed in the Gray-Level Co-occurrence Matrix. Theprocess of guiding a calculation by stratigraphic dip is calleddip-steering.

Texture analysis requires a high degree of resolution in stratigraphicgeometry to properly steer the Gray-Level Co-occurrence Matrixcalculation. To achieve the required resolution, the multi-trace, image,nature of the texel is exploited, and dips within an image are estimatedvia a gradient-based technique. The first step in this techniquerequires calculation of the horizontal (dx) and vertical (dy) gradientof pixel values within the image. The local dip of the reflectors isthen calculated by $\theta = {\tan^{- 1}\left( \frac{y}{x} \right)}$

The ratio dy/dx has units of time per cdp. However, for reasons ofconvenience, these units can be ignored and the dip can be expressed interms of pseudo-degrees relative to a horizontal time-slice.

Finally, as an optional step, a user-defined median filter can beapplied to remove noise. Once reflection dip is known everywhere withinthe texel, the Gray-Level Co-occurrence Matrix calculation utilizes thedip to guide the look-azimuth for each pixel to pixel comparison. Areasof steep dip are poorly imaged with the non-dip-steered Gray-LevelCo-occurrence Matrix calculation method. Alternatively, the negativeeffects of steep dip can be minimized by flattening or dating the volumealong a stratigraphic layer, before performing the facies analysis.

The method of the present invention does not require the use of welldata as a calibration. This is an advantage in exploration and earlydevelopment arenas where few wells are available for well-seismiccalibration. A calibration can always be, and in general is, performedafter the calculation. However, it is not required for application ofthe method of the present invention. Other methods using seismicattributes and neural networks generally require correlations betweenseismic and well data.

The present invention has been used to generate seismic facies volumesfrom standard seismic amplitude data. It has also been used onvolumetric AVO (Amplitude Versus Offset) attribute data such asslope-intercept volumes.

Although multiple textural attributes are calculated and used for thefacies classification, all required attributes are calculated as needed(on the fly) in the present invention. Thus, only the seismic volumebeing classified, the facies and probability volumes are stored at anygiven time. No textural attributes or other volumes are created. Thisprovides an advantage in not requiring large amounts of data storagespace for the present invention.

The present invention is capable of mapping seismic facies on a singleline or through a 3D volume. The ability to transform standard seismicamplitude or attribute volumes into seismic facies volumes results insignificant time reduction, improved accuracy, and reproducibilitywithin the seismic interpretation process. Seismic facies volumes areused for general analysis of reservoir geometry and continuity, for wellplacement, and to condition geologic models for use in developmentplanning and reservoir management. Example

The results of a regional study illustrate the effectiveness of thepresent invention. FIG. 2 shows a seismic cross-section, as selected instep 102 of FIG. 1. FIG. 3A shows polygons selected for texturalanalysis, as used in step 103. The corresponding facies classificationsare shown in FIGS. 3B-3G, as used in step 106. FIG. 4 shows a resultingfacies classification section, as calculated in step 109. FIG. 5 showsthe dip section used in dip-steering the FIG. 6 shows a correspondingconfidence section, as calculated in step 111. Low confidence values canbe observed in and near fault zones, where stratigraphic and structuralinteractions complicate the facies interpretation. Finally, FIG. 7 showsthe seismic facies volume for this example, as calculated in step 110.

It should be understood that the invention is not to be unduly limitedto the foregoing which has been set forth for illustrative purposes.Various A modifications and alternatives will be apparent to thoseskilled in the art without departing from the true scope of theinvention, as defined in the following claims.

What is claimed is:
 1. A method for identifying seismic facies in avolume of seismic data, comprising the steps of: (a) calculating aplurality of initial textural attributes representative of the volume ofseismic data; (b) constructing a probabilistic neural network from thecalculated initial textural attributes; (c) calculating faciesclassifications in a portion of the volume of seismic data; (d)repeating steps (a) through (c) until the calculated faciesclassifications in the portion of the volume of seismic data aresatisfactory; and (e) calculating facies classifications throughout thevolume of seismic data using the constructed probabilistic neuralnetwork.
 2. The method of claim 1, wherein the step of calculatinginitial textural attributes comprises the steps of: selecting at leastone cross-section of the volume of seismic data; constructing aplurality of polygons on the selected cross- sections; and calculatinginitial textural attributes from images in the constructed polygons. 3.The method of claim 2, wherein the step of calculating texturalattributes comprises the steps of: constructing Gray-Level Co-occurrenceMatrices from the images in the constructed polygons; and calculatinginitial textural attributes from the constructed Gray-LevelCo-occurrence Matrices.
 4. The method of claim 3, wherein the step ofconstructing Gray-Level Co-occurrence Matrices further comprises thestep of: constructing a volume of dip values from the volume of seismicdata; and applying dip-steering using the dip values.
 5. The method ofclaim 1, wherein the step of calculating textural attributes comprisesthe steps of: positioning a moving window throughout the volume ofseismic data; and calculating the textural attributes in themoving-window.
 6. The method of claim 1, further comprising the step of:constructing a volume of confidence values from the constructedprobabilistic neural network.
 7. The method of claim 6, furthercomprising the steps of: selecting a confidence level; and adjusting thesize of the moving-window to keep the confidence values above theselected confidence level.
 8. The method of claim 1, further comprisingthe step of: displaying the classified textural attributes.
 9. Themethod of claim 1, wherein the seismic data comprises seismicamplitudes.
 10. The method of claim 1, wherein the seismic datacomprises seismic attributes.